Author: infinite_uptime

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Condition Monitoring

Predictive Maintenance as a Service: A game-changer for Manufacturing.

Predictive Maintenance as a Service: A game-changer for Manufacturing.

Manufacturing encompasses a diverse and wide array of processes, industries, and raw materials. Yet all manufacturers everywhere share a common enemy: Unplanned Downtime, which harms productivity, asset health, brand reputation & dotted line.

Every year, top fortune 500 manufacturing enterprises lose almost 1 trillion to unplanned downtime, nearly 8% of their annual revenue. Here is how the introduction of Predictive Maintenance as a Service can be a gamechanger in the world of manufacturing & asset maintenance to curb unplanned downtime.

But to understand why Predictive Maintenance as a service is so revolutionary, let’s understand how asset maintenance was performed until a few years ago:

What are the types of Asset Maintenance practices in Manufacturing?

Reactive Maintenance: Reactive Maintenance means letting your machines run unchecked till they fail. Maintenance here is post-failure, as a reactive approach after the anomaly. While it saves you unnecessary downtime & maintenance costs for parts that don’t require servicing, it also means you risk machine failure anytime by being blind about your machine health.
Planned Maintenance or Preventive Maintenance: After the reactive maintenance approach resulted in constant fire-fighting for the plant manager, maintenance became a time-based activity, i.e., annual, bi-annual, based on their own and peer’s experiences. But often, it was noted that a planned downtime, although revealing nothing wrong with the asset, would still result in loss of productivity & profits. And sometimes, the machines would fail even before the planned period, so the problem persisted.

With the failure of both of these approaches to curb machine failure and thereby unplanned downtime in time, the industry looked forward to solutions like IoT & AI to power up maintenance with real-time insights. And that is what predictive maintenance as a service is all about.
Predictive Maintenance as a Service: Predictive Maintenance (PdM) relies on real-time monitoring of machine health using smart technologies like edge-computing, IIoT, data science, and analytics. Once an anomaly (w.r.t vibration, temperature, or acoustics) is detected, it is flagged off to the relevant plant supervisor for the next immediate action. A maintenance activity can thus be scheduled if something goes wrong while the maintenance expert can also decode the exact ‘something’. PdM enables the maintenance teams with necessary controls to extend equipment lifecycle, optimize the cost of maintenance, maximize machine uptime and amplify factory performance.

Types of machine maintenance in mining What Industries can Predictive Maintenance as a Service make the most impact on?

While any manufacturing plant- whether discrete or process-based can deliver a clear impact with Predictive Maintenance as a Service measures, the process-based manufacturing plants can truly thrive because of their unique workflow of interconnected processes.

Since the output of the process manufacturing plant depends on the previous steps completed in tandem, the stoppage of even a single machine can halt the entire production process. This is where predictive maintenance as a service can help by ensuring that the machine health issues are taken care of before they become serious.

Here are some examples of plants where Predictive Maintenance as a Service can save the day:

  • Cement plants
  • Steel plants
  • Metals & Mining
  • Oil & Gas Refineries
  • Power plants
  • Chemical plants
  • Pharmaceutical plants
  • Petrochemical plants

How can Predictive Maintenance as a Service be a gamechanger for Manufacturing?

Predictive Maintenance as a Service brings all the benefits of cutting-edge technology without the financial downside of capital intensiveness and sustainability. Here is how it can do magic for manufacturing plants:

Asset health & performance: In an asset-intensive industry like manufacturing, where the equipment is costly and used to the extreme, equipment & component replacement costs are prohibitively high. You can boost asset life, RUL (Remaining Useful Life), and Machine Uptime & Reliability by tackling asset issues before they get serious.

EHS & Compliance benefits:
Manufacturing plants consist of the most demanding working environments with toxic gases, material, and dangerous machines working furiously. It is no wonder that the regulatory guidelines get stringent now and then. It is also a risky working environment for the operators and other employees. Predictive maintenance as a service policy ensures no untoward accidents or compliance issues. Read more on our ATEX Certification.

OEE: Standing for Overall Equipment Effectiveness OEE is a globally prevalent metric that measures the productivity of a manufacturing asset. Calculated as a product of equipment availability, performance & quality of output produced, OEE is a benchmark for comparing the productivity of plants. The availability & performance of the machine depends on the maintenance & servicing when it is needed. According to a Deloitte study, a regular PM results in high OEE, Uptime & Reliability, compared to all types of maintenance.
Quality & Brand reputation: Regular Asset maintenance and machine health analysis can ensure that the machine performs at the top of its capacity. This will provide a high quality of the overall output. A fully functional plant producing quality output with minimal disruptions will also ensure a good brand image & reputation in the ecosystem.
Increased Employee Productivity: A well-functioning asset means that employees don’t have to fight fires caused due to last-minute machine failures. It also means quality and timely output, allowing them to be productive at what they do.

What Infinite Uptime’s Predictive Maintenance as a Service brings to the table?

The end-to-end Predictive Maintenance as a service by Infinite Uptime involves collecting data & computing the triaxial vibrations, temperature, and noise of the mechanical equipment in real-time via its patented edge computing system. The data is then monitored & analysed in real-time, and a machine health score is assigned. A machine with a lower health score is flagged to the plant supervisor or plant engineer with a diagnostic assessment score and the probable cause for the anomaly and a recommendation on improving the machine. This helps the maintenance teams to plan better and save critical downtime of machines which positively impacts the overall factory performance and productivity.

Conclusion

With real-time insights from interconnected assets being monitored and analysed instantly, predictive maintenance as a service provides massive power to the manufacturers without any drawbacks of a conventional maintenance solution. And that is when the true digital transformation will happen when data and insights combine to provide value.
Categories
Cement Industry

Predictive Maintenance as a Service for Cement Industry: An Overview

Predictive Maintenance as a Service for Cement Industry: An Overview

The cement manufacturing industry is one of the oldest and most critical manufacturing industries for the global civilization. It has witnessed unparalleled growth at the heart of most economic developments and international growth this decade. Fortune Insights report says, the global cement market will grow from $326.80 billion in 2021 to $458.64 billion in 2028, a steep 5.1% globally. It is then no wonder that cement plants face pressure for process and asset maintenance.

CEMENT MANUFACTURING PROCESS & NEED FOR PREDICTIVE MAINTENANCE

Cement manufacturing is one of the most complex continuous manufacturing processes, with multiple ingredients & steps involved. Here is an overview of the entire process wrt machines used at each stage:

Predictive Maintenance checklist for cement industry:

  1. Extractors: Used to Quarry the raw materials, i.e. limestone & clay
  2. Crushers used to crush high rock piles into coarse powders called raw meal
  3. Blenders & Mixers mix the crushed raw meal in the right proportions
  4. Grinders to further grind the raw material to free different minerals in the ore
  5. A rotary kiln where the raw meal is heated up to 1450 degrees & then cooled
  6. Assembly belts & conveyors to carry the cement for packing & dispatching to customers


These processes & machines need to occur in tandem, without intervals, to create high-quality cement. Unplanned downtime in even one of these machines can unleash havoc on the ongoing process, not just endangering efficiency & quality but also health & safety of personnel on-site.

Common causes for machine downtime in a cement plant

  • Loose nuts, bolts, springs, plates, spring rods, flywheel, bearings, shaft, coupling housing, hammer rotor
  • Motor failure, Conveyor belt, breakage, bearing failure, stretching rod breakage, breakage of separator blade
  • Fan bearing breakage, fan unbalance
  • Gear knocking, gear tooth wear, gear deformation, gear spitting and spalling
  • Axle spindle breakage, crusher bearings failure, slip tape breakage
  • Disc liner shift
  • Rolling mill cracks, tubing failure, pump failure, spoke breakage
  • Grate plate breakage

Why asset maintenance in cement plants is a necessity?

Asset maintenance in cement plants is critical because:

  • Extensive repair & replacement costs
  • Chances of industrial safety hazards & accidents
  • Over maintenance of equipment, causes wear & tear
  • Harsh operating environment
  • Dynamic environment, needing proactive decision making
  • Enable remote monitoring & control for agility & resilience to

How can Predictive Maintenance as a Service help?

With the stakes so high and a constantly changing environment, real-time machine diagnostics are necessary to empower plant managers with the correct data. IIoT can enable this by enabling a 360-degree view of interconnected assets across the plant. Predictive maintenance as a service allows plant managers in cement managers to move away from reactive measures like reactive maintenance and preventive maintenance to a predictive one, where critical machines don’t have to be pulled down unless there is a specific anomaly.

At a grass root level, predictive maintenance as a service by IU for cement plants can be implemented by putting sensors at strategic positions on the machines. Vibration analysis of mechanical equipment components like Air Compressors, Belt drives or Conveyors, Fans and blowers, Kiln rollers, Motor bearings & Vertical and horizontal mills can help predict anomalies.

The Predictive Maintenance as a service solution by Infinite Uptime involves collecting data, analysis & computing of the triaxial vibrations, temperature and noise of the mechanical equipment on edge at real-time via a patented edge computing system. The data then is monitored & analyzed in real-time, and a machine health score is assigned. A machine with a lower health score is flagged to the plant supervisor or plant engineer with a diagnostic assessment of the probable cause for the anomaly and a recommendation on improving the same. Not just that, if not considered severe yet, but still significant; the fault is continuously monitored, with relevant parameters like temperature, vibration etc., to assure that it does not aggravate the status quo. This information can be made available in real-time to the appropriate people at their fingertips. An access-based dashboard ensures that you get access to the most relevant machine data for the plant from single machine access for a plant operator to multiple machines across the plant access for a plant head and a multi-plant machine score for a manufacturing head. Let’s look at a case study around how we helped a top Indian cement manufacturer reduce 250 hours of downtime.

Conclusion

Today, the cement industry is on the cusp of digital transformation, fueled by rising demand and cut-throat competition and increasingly stringent regulations. The pressure on the cement industry’s assets, processes, and people to be on the top of their game has never been higher. In such a scenario, Predictive Maintenance as a Service for your cement plant can help avoid machine failures and the associated unplanned downtime and the quality of the output cement and the OEE (Overall Equipment Effectiveness) of the cement plants. It improves machine availability and performance, also saving costs for repairs & spare parts. But most importantly, it arms you with resilience & agility during unpredictable times via remote monitoring and proactive maintenance when needed the most.
Categories
IIoT

Complying with ATEX standards in hazardous environments. Why does it matter?

Complying with ATEX standards in hazardous environments. Why does it matter?

Manufacturing facilities are no less than war zones – they have difficult workplace conditions like explosive atmosphere, flammable & toxic gasses and combustible substances. A few more hazardous than others are like Oil & Gas, Petrochemical, Chemical plants & Power plants. Such a high-risk workplace environment is safeguarded by mandatory health & safety risk assessments, certifications, safety gear, rules & regulations. It ranges from what kind of devices can be used on-site to the gear worn by the workers.

An ATEX certification for your equipment can be a gamechanger. This article tried to address the most common questions around ATEX certification.

What is an ATEX Certification?

ATEX stands for ATmosphere EXplosible.
It certifies equipment & protective systems intended for use in potentially explosive atmospheres. It categorizes equipment based on its protection against turning into an active ignition source. Here are the two European Directives for certifying equipment that is declared ‘intrinsically safe’ in the explosive atmospheres:

  • Directive 1999/92/EC (also called ‘ATEX 153’ or the ‘ATEX Workplace Directive’)
  • Directive 2014/34/EU (also called ‘ATEX 114’ or ‘the ATEX Equipment Directive)

The ATEX 2014/34/EU is the new accepted safety standard for testing & certifying equipment intended to be utilized in potentially explosive environments in the EU, post a 2015 Legislative change.

The ATEX certification covers explosions from flammable gas/vapours and combustible dust/fibres (which can also lead to explosions)

Here are how zones for flammable gas/vapour (a potentially explosive atmosphere consisting of air with a mix of toxic substances in the form of mist/vapour/gas) are classified for ATEX certification:

  • Zone 0 – A place where a potentially explosive atmosphere is present continuously or for long periods.
  • Zone 1 – An area in which a potentially explosive atmosphere is likely to occur occasionally.
  • Zone 2 – A place where a potentially explosive atmosphere is not expected to occur usually, but if it does happen, it will persist for a short period only.

Identifying an ATEX certified Equipment

If equipment has an official ATEX certification, it has been completely certified to be safe for being used in hazardous/explosive atmospheres. ATEX approved equipment can be identified by the official ‘Ex’ logo shown in the image above.

Any equipment without Atex certification must not be brought onto site in manufacturing facilities with an explosive atmosphere to prevent any probability of disasters.

Why is ATEX Certification challenging to achieve?

For a product to be ATEX certified, it must undergo rigorous tests quality checks for weeks & even months in various test conditions. Even after the certification is processed, quality assurance, compliance checks, and audits are conducted to ensure that the product complies with the stringent benchmarks.

Once the certification is provided to a product, even a tiny tweak or alteration to the product in any form can render the certification null & void.

Is an ATEX Certification applicable across the globe?

Although initially constituted by the European Union for its member states, the ATEX certification is slowly gaining global acceptance as a preferred standard for accepted devices in potentially explosive atmospheres. OEMs with ATEX Certifications now find interested buyers even outside the EU, and it is predicted that it may one day become the globally accepted standard.

Conclusion

If you are looking for equipment to be used in a plant with a potentially explosive environment like the industries mentioned above, then looking up an ATEX certification first will go a long way in finding reliable equipment.

At Infinite Uptime, we very well understand the importance of HSE initiatives in manufacturing and how every small bit of diligence is critical to ensuring a risk-free environment & safety of the workers. To this end, we are delighted to share that vEdge, our edge computing technology that enables our Diagnostics Service, has received a Zone 0 ATEX Certification from the International Centre for Quality Certification (ICQC LLC), Latvia. Here is the ATEX-Certification for our vEdge. Want to know more about how vEdge can propel digital reliability even in the most difficult environments?

Reach out to our team at contact@infinite-uptime.com

Categories
Industrial Analytics

Impact of Industrial Analytics in Fostering Manufacturing Transformation

Impact of Industrial Analytics in Fostering Manufacturing Transformation

If data is oil, manufacturing is the brightest lamp powered from it. Manufacturing is expected to generate 1812 Petabytes (PB) of data every year, a lot more than BFSI, healthcare & many other industries, according to Deloitte. Industrial Analytics today are helping optimize every facet of manufacturing by enabling proactive decision making & automation across organizations through the access of the right data to the right people on time.

What does Industrial Analytics exactly do?

Industrial Analytics collects, analyses, and uses data generated in industrial operations through machines, processes, and people.

Traditionally Manufacturers have always been using data to improve their efficiency & machine health for years. But what has changed now with technologies like IoT is how the data is captured. In the past, data collection was done manually, with plant operators recording data or feeding it in a machine. But these approaches are flawed- they are time-consuming and prone to human errors and biases. This data is still grassroots and not actionable for decision-making, particularly at a senior level. With digital transformation, tons of strategically placed sensors capture every critical machine data, recorded and analyzed in real-time. The level of insights that emerge is actionable for every level.
Here is how industrial analytics is transforming various use cases in manufacturing, improving the efficiency and productivity of machines, processes & people:
  • Improving Manufacturing Supply Chain

    • In a hyperconnected world, manufacturing processes and supply chains are getting increasingly extensive and complicated. Industrial Analytics enable manufacturers to hone in on every stage of the manufacturing process and study supply chains in minute detail, accounting for individual activities and tasks. Based on machine health, inventory status, forecasting of orders, preparation, and choice of suppliers can be made in advance.


    In the current scenario of a dynamically changing environment, a resilient supply chain can make or break your enterprise.
  • IReduce Downtime
Manufacturers can extend the life of critical assets by using data to predict when they will fail. Predictive maintenance systems today collect past data to produce insights that aren’t visible using traditional methods. For example, companies may utilize industrial analytics to identify the conditions that may cause a machine to malfunction and monitor input parameters to act before the equipment breaks or be prepared to replace it when it does, reducing downtime. Factors like Misaligned shafts, lubricant oil contamination and excessive vibrations can result in unplanned downtime if not controlled in time. Technologies like Infinite Uptime’s IDAP can allow plant managers to track these in real-time and predict anomalies with a prescribed solution, effectively minimizing planned and unplanned downtime.

Since machine downtime can cause a loss of around $260,000 an hour per hour for a manufacturing company, this is one of the most critical use cases for industrial analytics.
  • Productivity & Production process improvement
Improved workforce productivity and processes can be measured, monitored and optimized with suitable parameters via industrial analytics. With the efficiency of machines and processes in place, operator efficiency performance can be mapped to benchmarks to identify techniques that cause a decrease in operator performance at various stages of production.

On the other hand, Manufacturers can detect bottlenecks and inefficient processes and components that are causing them. Industrial analytics also reveals interdependence between different processes and their outputs, allowing producers to consider each process separately, improve manufacturing processes, and devise predictive maintenance procedures to address any stumbling blocks.
  • Drive machine OEE improvement
OEE improvement is a crucial metric for shopfloor performance. With effective Manufacturing analytics in place, components like asset utilization, efficiency, product quality rating and runtime for every machine can be tracked. This information in real-time can enable manufacturers to figure out the machines causing the bottlenecks in reaching the planned OEE. Mapping these key performance metrics at a plant level can help top-level decision-makers to make changes at an asset and process level to take the OEE to the planned level.

For the plant head & manufacturing head for multiple plants, it is easy to track performance vis-a-vis assets and entire plants to find what is performing well and what can be improved.
  • Reduce Manufacturing Errors
Industrial analytics can assist in reducing errors in manufacturing processes, operators and machines, improving the quality of the output.

For example, a perfectly aligned machine can perform at its best, producing quality output. Infinite Uptime’s IDAP helps ensure the machine is correctly aligned at all times and functioning at its optimal capacity.

With predictive & prescriptive industrial analytics, any potential downstream quality or equipment issues can be detected. Corrective action can be taken to salvage the quality, and in the case of discrete manufacturing, an intermediate product can be discarded to save further losses.

Conclusion

Industrial Analytics can thus be the key to unlocking hidden potential and business value in various parts of your manufacturing process.

Want to realize the potential of your manufacturing data? Reach out to us at contact@infinite-uptime.com to set up a call with our team of experts.

Categories
Predictive Maintenance

Top Manufacturing Trends of 2022

Top Manufacturing Trends of 2022

The last two years have been nothing less than a vigorous shake-up to manufacturing. From kickstarting an industry-wide awakening to digital transformation and remote operations for a decade, manufacturing has seen nothing less than a paradigm shift. It has also brought the spotlight on the criticality of this sector for the global economy & those who work tirelessly day & night in factories.

In 2022, we expect all of these trends to rise. In the past two years, the challenges experienced in industrial operations have led to a complete introspection of the entire manufacturing operations cycle, including supply chain management & asset maintenance. The results gradually create a need for a fundamental change to protect the bottom line while staying agile & resilient to any future challenges.

Here are some top trends for Manufacturing in 2022:

1. The Remote Monitoring shift: It’s not that remote monitoring has not existed for Manufacturing, but it was the choice of a few digitally aware pioneers. But intermittent surges of the pandemic, with its restrictions of social distancing, workforce shortages, and government regulations, have made remote monitoring mandatory for Manufacturing. The hybrid or remote working setup is not a temporary shift anymore.

With remote work becoming the new normal, investing in technologies like IoT that make remote monitoring happen is now essential for digital reliability & empowering your maintenance team to stay prepared for all circumstances. A platform like Infinite Uptime’s Industrial Data Enabler (IDE), a patented edge-computing Vibration monitoring system, can point out any anomalies in the machines long before they become critical. Accessing the correct data to the relevant people in real-time is no less than a game-changer. Here is how remote monitoring can help your facilities.
2. Connected Supply Chains: Supply chains were hit in the worst way due to the globe-wide nature of the pandemic. Unpredictable & sudden shortages globally, coupled with lockdowns, made inventory management very difficult. While businesses are trying to make their supply chains agile & localized, digital technologies like AI, ML & IoT are helping solve this via connected supply chains. Here are two ways how they can help:

  • By solutions that can predict the inventory needs in advance and enable real-time tracking of shipments, optimizing delivery timings
  • An intelligent Predictive Maintenance solution that ensures a consistent & reliable asset performance can predict the flow of raw materials products from supplier & the end product to the customers due to fewer breakdowns or disruptions.
3. Skill gap & rise of Prescriptive Analytics: With increasing competition & economic growth, shortage of skilled workforce is one of the biggest challenges for manufacturers globally. In addition to this, the retirement of baby boomers with years of information & tribal knowledge without a proper knowledge transfer can lead to huge errors on the shop floor. That is why today, your digital reliability system needs to be prescriptive, not just predictive. Not only does the anomaly in the machine need to be predicted, but a recommendation based on past data also needs to be suggested to the plant operator. Only then can manufacturing analytics become truly actionable in time.
4. Sustainability beyond compliance: With the rise in global initiatives around climate change and sustainability, every industry has been experiencing the impact. Manufacturing has not been any different. Sustainability for Manufacturing comes in various forms- for operations, energy footprint, water and packaging to reduce waste and carbon emissions. For the first two, reliable assets are the key. A well-functioning asset that is always available & performing well will not result in higher energy overheads. On top of this, sudden malfunctions or stoppages without an established protocol in the case of industries like chemicals, oil & gas, etc., can result in harmful emissions in the atmosphere.

Conclusion

The past two years have reiterated the importance of a data-driven approach coupled with automation and the importance of a healthy energy footprint, workers and machines. 2022 will see manufacturers globally investing in these trends and using innovative technologies to bridge the gap between today’s setup and industry 4.0.

About Infinite Uptime

Infinite Uptime is transforming the industrial health diagnostics space with a Digital First approach. We provide comprehensive solutions around Machine Diagnostics, Predictive Maintenance and Condition Monitoring to the top engineering and process industries globally. We promise to deliver maximum Machine Uptime, minimize Factory Disruption and elevate Equipment Reliability for a stellar factory performance.

Infinite Uptime leverages IoT, machine learning, artificial intelligence, smart communications, cloud computing, analytics and data science techniques to accelerate digital adoption and turn Industry 4.0 into a business reality. To know more about us and our customer success stories, please visit www.infinite-uptime.com or write to contact@infinite-uptime.com.

Categories
IIoT

IIoT-based predictive maintenance – A mission critical need for manufacturing

IIoT-based predictive maintenance – A mission critical need for manufacturing

Industry 4.0 continues to gain momentum across every industrial and manufacturing segment. This revolution is built upon three primary technologies: Big Data, Edge Computing and the Internet of Things (IoT). As the adoption of IoT devices continues to grow, many organizations are switching to edge technology because of its advantages over legacy cloud solutions. One of the key advantages of edge computing is real-time predictive maintenance. In a predictive analytics solution, Artificial Intelligence (AI) is combined with Business Intelligence (BI) to monitor the operating condition and predict when to perform maintenance on that asset.

What is Predictive Analytics?
Predictive analytics uses statistical algorithms and advanced analytics combined with AI techniques to predict future outcomes based on historical and current data patterns. Organizations use this method to benefit possible future events by using predictive modelling to take maintenance decisions before a disruptive event. This technique imports data from the targeted asset synthesizes it and combines it with different data sources. Once a large amount of data is cleaned, the data analysis is initiated to recognize patterns and trends. In simple words, using Artificial Intelligence and Machine Learning technique, a machine can predict future events.

What is Predictive Maintenance? A subset of predictive analytics, predictive maintenance is the process of utilizing data analysis to predict future outcomes. This technique is used to recognize potential faults in machines and processes. Manufacturing and service industries need to improve the performance of their assets. As per the report by a leading publication, spending on IoT-enabled predictive maintenance will reach 12.9 billion by 2022 compared to $3.4 billion in 2018.

Benefits of Predictive Maintenance:

An AI-enabled predictive maintenance solution comes with numerous competitive advantages as compared to legacy maintenance processes.
1. Improved Machine Lifespan: By identifying problems, machines can be serviced even before the problem occurs. Also, with a constant study of the machine, the AI solution prevents any significant damage from occurring, consequently improving the overall health of connected equipment and uptime its average lifespan.
2. Increased Production: With the ability to constantly monitor a machine’s performance, one can avoid unscheduled downtimes and improve operations throughput. This not only improves the machine’s health but also enhances the quality of the production.
3. Minimize Maintenance Costs: With the help of IoT sensors, it becomes easy to detect anomalies and repair them before the problem becomes irreversible. This minimizes the chance of operational setbacks due to unplanned machine downtime. A report by McKinsey suggests that a predictive maintenance application can minimize maintenance costs by 25%. On the other hand, Deloitte believes it can reduce machine breakdowns by 70%.
4. Reduction in Downtime: A predictive maintenance solution can cause approximately a 45% reduction in downtime. The analytics provide insight on faults and require repairs so you can schedule them accordingly. This helps companies to effectively optimize their resource schedules or schedule maintenance outside of operation hours.
5. Improved Benefits: The data collected from the IoT-based solution helps businesses make practical and calculative decisions regarding machine management. This can improve manufacturing value by enhancing the overall equipment effectiveness and the production volume. This can also decrease replacement or repair costs. Businesses are leveraging IoT-based predictive maintenance to improve value and minimize costs.

The Future of Predictive Maintenance

Although cloud computing can support predictive analytics systems, organizations gain a crucial advantage by refining data analytics and processing speed and performance through edge computing. A predictive maintenance solution performed at the edge minimizes data storage costs along with real-time analytics and low latency. IoT devices and sensors gather data frequently, meaning these IoT-enabled solutions work with enormous data.

When we implement such solutions through cloud computing, vast data gets shared over the network to the cloud. While the load on the internet continues to grow, the cost of networking will increase as well. Predictive maintenance solutions, run on the edge analyze the data on-premise in real-time to minimize the amount of data shared on the cloud, saving businesses money on cloud storage costs.

About Infinite Uptime

Infinite Uptime is transforming the industrial health diagnostics space with a Digital First approach. We provide comprehensive solutions around Machine Diagnostics, Predictive Maintenance and Condition Monitoring to the top engineering and process industries globally. We promise to deliver maximum Machine Uptime, minimize Factory Disruption and elevate Equipment Reliability for a stellar factory performance.

Infinite Uptime leverages IoT, machine learning, artificial intelligence, smart communications, cloud computing, analytics and data science techniques to accelerate digital adoption and turn Industry 4.0 into a business reality. To know more about us and our customer success stories, please visit www.infinite-uptime.com or write to contact@infinite-uptime.com.

Categories
Condition Monitoring

Digital Transformation – The Future of Predictive Maintenance

Digital Transformation – The Future of Predictive Maintenance

Digital Transformation or digitization is no longer just a hype or a concept. It has found a pertinent place in every facet of business and companies who want to build futuristic business model. The advent of these disruptive technologies is proving to be a game-changer and driving a digital revolution in the industrial and manufacturing space. Conventional manufacturing industries are rapidly transforming into digitally connected enterprises by adding OT (Operations Technology) & IT (Information Technology) muscle to their production processes, manufacturing capabilities and maintenance programs.

In simple words, mechanical data and insights generated by applying these technologies are helping industrial setups to control cost, improve speed to market, drive critical decisions, add customer-centricity, and create competitive advantages.

Challenges in the mining indApplying Digital Transformation as a Service (DTaaS) in manufacturing ustry

Predictive Maintenance (PdM) and Condition Monitoring (CM) are the most sought-after and top opportunities in the industrial ecosystem from a return-on-investment standpoint. Digital transformation in PdM & CM is the application of advanced computing technologies to enhance the overall machine efficiency, reliability, outcome and sustainability in manufacturing operations. It combines a collaborative approach of man-machine-technology ecosystem along with all its service components.

Benefits of DTaaS in Predictive Maintenance and Condition Monitoring

DTaaS provides reliability-engineering teams with a plethora of new tools & approaches to effective machine maintenance and opportunities to accelerate transformation and revenue growth for the top stakeholders. Here are some direct benefits of converting the data & insights from a digital system to operational performance:
  • Save 30-50% or more on maintenance cost
  • Increase machine uptime significantly
  • Improve visibility in maintenance programs and manufacturing processes with accurate machine data
  • Build a connected enterprise with a man-machine-technology ecosystem
  • Create a digital drive in your enterprise to achieve Industry 4.0 objectives


DTaaS influences multiple aspects of the industry irrespective of the business competency, production facility and promotes coherent integration with hardware and modern technology such as IoT, Artificial Intelligence, Machine Learning, Cloud Computing, etc. The cost-effective yet endless services signify improving existing processes, nurturing ideas, over-ruling cultural barriers, and providing a generic interface with the professionals.

The emerging service model enriches customer experiences and focuses on customer connectedness. The excellence of the services is directly related to data collection, vibration monitoring, predictive analytics, visualization and reporting. Developing a strong bond with the customer at a functional and business level to promote an adaptive and data-guided mindset encourages better alignment of the available infrastructure and resources.

Begin your Digital Transformation journey with Infinite Uptime

DTaaS resolves the complexity of Digital Transformation that depends on IT integrations, extraordinary consultations, considerable upfront costs, and the relative value-addition at different stages. 95% of industry experts agree that DTaaS in manufacturing is essential to create a broader business impact and create a deeper business relationship. Infinite Uptime has been extending its DTaaS services with Diagnostics to all the engineering and manufacturing industries.

The services comprise of monitoring, collecting and analysing mechanical (machine or equipment) data using a patented technology called, vEdge, an Industrial Data Enabler (IDE) and Industrial Data Analytics Platform (IDAP). This empowers the maintenance authorities, CBM experts and maintenance heads to perform in-depth analysis and diagnoses the root cause of discrepancies in the machine.

These real-time machine health diagnostics solutions enabled with connectivity, communication, machine analytics, insights and reporting are driving innovation in manufacturing. Industrial IOT, AI, ML, Cloud and data analytics is rapidly accelerating strategic C-Suite objectives like Smart Manufacturing, Industry 4.0 Transformation and creating a Digitally Connected Enterprise.

Looking for expert guidance on how you can start your digital transformation journey or worried whether this will benefit your business?
Write to us at contact@infinite-uptime.com and we will ensure you get answers to all your questions.
Categories
Predictive Maintenance

Understanding Predictive Maintenance As a Service

Understanding Predictive Maintenance As a Service

Asset maintenance in discreet and process manufacturing is a key priority – one that can have a multiplying effect on plant reliability and bottom line. For industries where even a single hour of production downtime due to asset unavailability can translate into millions of dollars of losses, strategic investments are made to make maintenance more effective. Maintenance management has thus, evolved into a complex field with application of varied digital and analytics solutions to improve efficacy while reducing costs.

Predictive maintenance is a result of this pragmatic evolution and has helped manufacturers save billions of dollars through increased Remaining Useful Life (RUL) of assets and minimized production downtime. By 2025, the predictive maintenance market is expected to grow to USD 25 billion, creating unparalleled value across global industrial value-chains. This article will cover what predictive maintenance is, how it has evolved to suit industry needs, and how it applies in various manufacturing setups.

 

What is Predictive Maintenance?

Predictive maintenance (PdM) is a maintenance approach which entails use of cloud-enabled technologies to monitor diverse assets involved in production and estimate the maintenance needs on the basis of asset condition and detected anomalies. Condition Based Monitoring (CBM) is the underlying concept, which is used to monitor industrial assets in real time. Technologies such as infrared thermography, vibration analysis, oil analysis, and acoustic monitoring are deployed to collect equipment condition data.

The data thus collected is analysed to detect any deviation in asset performance or irregularities which are otherwise impossible to detect without sophisticated equipment. But predictive maintenance doesn’t just stop at detection of performance issues. With the help of edge diagnostics and predictive analytics, the underlying causes behind poor asset performance are determined. Predictive maintenance solutions can also forecast the time when a monitored equipment will breakdown, if corrective maintenance measures are not taken.

Historically, industrial maintenance has evolved from being reactive to predictive.

This evolution has been driven by an increased focus on Overall Equipment Effectiveness (OEE) and the need to reduce unplanned production downtime. While manufacturing plants no longer rely on a reactive maintenance approach, preventive or scheduled maintenance is the most popular among undigitized setups. Since a preventive approach can deliver 50%-75% OEE on an average, maintenance leaders hesitate to shift away from it towards a predictive maintenance approach. However, understanding the key differences between both practices can help.

How is Predictive Maintenance Different from Preventive Maintenance?
Predictive Maintenance Different from Preventive Maintenance

Preventive Maintenance

  • Preventive maintenance is essentially a periodic and schedule-based maintenance approach that uses asset performance history and routine equipment inspections to plan maintenance events.
  • Irrespective of the machine condition and the remaining useful life of the asset, maintenance and replacement activity is performed. 
  • Preventive maintenance is usually labour-intensive and not very cost-efficient. Maintenance teams have to maintain inventory of equipment spare parts and schedule planned downtimes to execute maintenance.
  • The net cost of asset is increased as over the period of its usage, several parts are replaced and consumables are used to keep the asset running optimally.
  • Latent problems that may develop in-between inspection schedules could go un-noticed and remain un-addressed. As a result, the dependence on reactive maintenance increases.

Predictive Maintenance

  • Predictive maintenance relies on continual monitoring of equipment condition through sophisticated technologies that are IoT-enabled.
  • Maintenance activity is highly targeted in predictive maintenance and only performed when a fault or anomaly is detected. Routinely organized production downtimes and pre-emptive part replacements are not required.
  • PdM is increasingly efficient over time and reduces the labour-intensive nature of maintenance activities. Predictive maintenance is also more cost-efficient, reducing the inventory carrying burden for spare parts.
  • Net cost of assets is effectively reduced as the complete useful life of every machine is utilized before any replacement is done.
  • The likelihood of unplanned reactive maintenance is reduced by 70-90% with a predictive approach. Since equipment faults are diagnosed well before the actual functional failure, organized maintenance events can help avoid process disruption and reactive efforts.
  • Critical equipment problems and underlying causes of diminishing productivity can be accurately identified with predictive maintenance. As a result, a higher network system reliability can be achieved in a cost-efficient manner.

To better understand how both the approaches can have a distinctive impact on the maintenance organization, here’s an example. A single high-pressure roller press can support the production of a 2MTPA cement grinding plant. If it breaks down due to undetected anomalies or fault in its planetary gearbox, it can result in a potential downtime of 24-48 hours. Now, preventive maintenance measures allow plant maintenance staff to schedule regular asset maintenance events, irrespective of the machine condition and abnormalities which may be affecting productivity.

On the other hand, with a predictive maintenance approach, not only can such anomalies be detected in advance, but such critical events can also be avoided with prescriptive measures. (Read the full case here.)

How does Predictive Maintenance Work? While predictive maintenance is applicable for both discreet and process manufacturing setups, its adoption must follow a strategic workflow. The following process or workflow can help manufacturing maintenance teams effectively harness the advantages of predictive maintenance:
predictive maintenance process

To begin with, all assets available in a production environment must be evaluated to determine how mission critical they are and whether they should be proactively maintained. Once the appropriate machine groups are identified, they can then be outfitted with the cloud-enabled predictive maintenance solutions. These solutions could offer edge-diagnostics with the help of vibration analysis and condition monitoring, but before fault diagnosis can happen, it is important to define performance parameters for assets being monitored.

These performance parameters can include baseline metrics which would be considered as the norm, and continual monitoring would not lead to unnecessary false positive notifications. The assets can then be allowed to operate as per their pre-defined schedule and requirement. Meanwhile, real-time performance data and machine health indicators can be collected and stored on cloud. Advanced predictive analytics can diagnose data anomalies and generate insights regarding potential faults in the monitored assets.

These insights can empower maintenance teams to schedule maintenance activities with minimal production downtime and costs.

 Conclusion:

In sum, predictive maintenance as a service has evolved with the core objective of making industrial asset management more efficient and less costly. Data accessibility and intelligent analytics have shaped the service into a powerful enabler that allows maintenance and operations teams drive plant reliability objectives. While predictive maintenance adoption has to be strategic and supported by change management, its benefits are quickly felt and undeniable for even the most complex production units.

Infinite Uptime offers responsively designed predictive maintenance solutions in diverse industries such as Cement, Steel, Mining and Metals, Tire, Paper, Automotive, Chemicals, FMCG, Oil and Gas, and more. To understand how predictive maintenance applies to your process plant, explore the digital reliability [PS1] and asset management solutions of Infinite Uptime.

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Digital Reliability

Understanding Digital Reliability

Understanding Digital Reliability

Understanding Digital Reliability

Understanding Digital Reliability

Manufacturing industries manage complex production environments where even a single hour of downtime caused by unreliable or unavailable assets can cost the manufacturer millions. Not to mention, there are added risks of hazardous leaks, life-endangering accidents, and a complete breakdown of the value chain. One of the reasons behind the uptake of Industry 4.0 technologies and systematic digital transformation is the need to overcome this inherent uncertainty in discreet and process plants.

Intelligent automation and advanced analytics can enable operation and maintenance teams to improve asset availability in their plants. Furthermore, mission-critical assets can be made more reliable, helping plant teams achieve intended targets and objectives. This is why there’s an increasing shift in focus toward digital plant reliability and manufacturing leaders are prioritizing the adoption of technologies that can drive these objectives.

This article will cover what digital reliability is, what are its benefits, and which technologies are driving digital reliability in manufacturing industries.

What is Digital Reliability?

Reliability is the probability of a system meeting certain pre-defined performance standards and delivering desired output for an intended period of time. A reliable system continues to perform within specific parameters, without experiencing any anomalies or breakdowns and operates at optimum productivity level. Digital reliability is ensuring asset and plant reliability through smart digitalization of processes and increased data availability to support decision-making.

Contemporary digital reliability solutions rely on real-time condition monitoring of manufacturing equipment, performing predictive analytics on collected data, and mapping the machine performance to generate a realistic health status of the asset. Data irregularities are investigated to diagnose existing or potential faults, and take corrective measures to mitigate the risk of failure.

Take a steel manufacturing plant, for instance, where a cold rolling mill is critical equipment that controls the production flow and throughput quality. The asset is responsible for achieving greater dimensional accuracy and increasing the hardness of the final product. If a cold rolling mill is available but not functioning under optimal conditions, then potential breakdown can lead to several hours of production downtime. With digital reliability measures, this eventuality can be avoided and the cold rolling mill can become highly reliable, saving over 72 hours of production downtime. (Read the full case here.)

Benefits of Digital Reliability

Digital reliability is becoming increasingly important in production environments as a lack of data-backed insights on machine health can diminish plant reliability, often resulting in:

Risk of Unreliable Asset in manufacturing

With responsively designed and implemented Digital Reliability solutions, these risks can be successfully mitigated. Plant maintenance and operation teams can intelligently put available equipment data to use and plan maintenance events to drive net productivity. The following benefits can be achieved with digital reliability in manufacturing setups:

Predictive Maintenance for Digital Reliability

More than 70% of equipment breakdown is due to mechanical faults, including equipment wear, deterioration, backlash, increase in clearances, vibrations, and acoustics. While for hydraulics, thermal and electrical faults, standard monitoring solutions are available, for mechanical faults, monitoring becomes challenging. To drive digital reliability objectives in these scenarios, predictive maintenance becomes an important enabler.

With Predictive Maintenance (PdM), plant maintenance teams can estimate the exact remaining useful life (RUL) of the equipment perform accurate diagnostics, and receive insightful recommendations to strategically plan maintenance activities. Real-time monitoring of triaxial vibrations, acoustics, and surface temperature is utilized to generate digital reliability reports and guide maintenance schedules. Responsive predictive maintenance solutions can also:

  • Adapt to special production conditions
  • Diagnose high-frequency data
  • Accurately decipher the signal from noise 
  • Overcome complex bandwidth limitations 
  • Collect and store multi-location data on-cloud

A predictive maintenance approach integrates naturally with the digital reliability objectives of any manufacturing setup. The monitoring objectives can be driven by digital reliability goals, and the final outcome of Predictive Maintenance activities can result in a feedback loop to refine and improve the digital reliability strategy.

Predictive Maintenance for Digital Reliability

 Conclusion:

In sum, digital reliability ensures that all available assets in a production environment perform reliably as per pre-defined standards and metrics. With cloud-enabled predictive maintenance technologies, sophisticated digital reliability solutions can be tailored to suit the specific needs of a manufacturing unit. Continuous asset monitoring, and data-backed decision-making for the maintenance and operational activities, strategically drive digital reliability and create more productive and secure production environments.

Infinite Uptime offers responsively designed predictive maintenance solutions in diverse industries such as Cement, Steel, Mining and Metals, Tire, Paper, Automotive, Chemicals, FMCG, Oil and Gas, and more. To understand how predictive maintenance applies to your process plant and can help in driving digital reliability, explore the asset maintenance and reliability solutions of Infinite Uptime.

 

Categories
Asset Reliability

Understanding Asset Optimization in Manufacturing

Understanding Asset Optimization in Manufacturing

Mission-critical assets in manufacturing setups can make or break an entire value chain. An unmitigated asset breakdown or productivity decline can halt production for hours, or even days, resulting in huge revenue losses and unsafe work environments. The situation becomes even more complex for industries with distributed assets.

To maintain assets in optimal condition and run the production process without disruptions, dedicated maintenance teams have to be deployed in various locations. Furthermore, investments are required in carrying spare-parts inventory and establishing strategic service contracts with Original Equipment Manufacturers (OEMs). While conventionally, these practices have been considered inevitable, a marked shift is happening towards predictive analytics and responsive maintenance solutions that can optimize asset performance.

This article will cover what exactly asset optimization is, why is it important for manufacturing industries, and how predictive maintenance solutions empower maintenance and operation teams to achieve it.
What is Asset Optimization?
Optimization essentially means making something as effective, functional, reliable, and productive as possible. Asset optimization means optimizing the way that an asset is utilized and deriving maximum value from it. It also entails driving efficiency and reliability objectives by improving the Remaining Useful Life (RUL) of an asset and enhancing the Overall Equipment Effectiveness (OEE).

Asset Optimization depends on leveraging data-driven intelligence and predictive analytics to achieve business objectives and add to the bottom line. IoT-enabled technologies can be deployed to monitor asset conditions and analyze real-time data to determine maintenance needs.
Benefits of Asset Optimization
Optimal asset performance and availability have a dramatic effect on the overall productivity and throughput of a production plant. When assets are operating in optimal conditions, the following benefits are derived in discreet and process manufacturing industries:
In addition to these, safer and accident-free production environments can be created, with reduced risk of catastrophic events that could cost life and property. An indirect, yet palpable effect is also observed on revenue, margins, customer satisfaction, Return On Assets (ROA), and Work-In-Progress (WIP) inventory.
Challenges in Asset Optimization Despite the incredible benefits that asset optimization offers, it is quite challenging to manage asset performance towards optimization. Major roadblocks in asset optimization are:
  • Maintenance frequency: When manufacturing plants adopt breakdown maintenance (till failure) or scheduled (preventive) maintenance strategies, asset conditions often remain less than optimal. Either maintenance is performed when an asset breaks down or is performed periodically, irrespective of what the asset condition is. In both scenarios, it is impossible to extract the maximum use of an asset.
  • Lack of data: Real-time information about asset conditions is rarely available, especially if manufacturing plants rely on offline asset inspections. Even when regular equipment inspections are performed manually, gaps remain in the data and many alarming signs about deteriorating equipment conditions may go unnoticed.
  • Costly unplanned maintenance: For industries with distributed assets, unplanned maintenance in the event of machine breakdown proves to be very costly. A larger maintenance team needs to be maintained to cover the geographic distribution of assets. Spare parts and sub-assemblies need to be sourced at higher prices to fulfill urgent requirements. Not to mention, on-floor conditions are highly unsafe and hazardous for maintenance workers.
  • Poor flow of information: Offline machine inspections and decentralized maintenance events create silos of information within the manufacturing organization. Critical information about asset conditions is not shared in real-time with all concerned stakeholders, and maintenance teams operate independently as per their capabilities.
  • Ineffective utilization of resources: Both human and physical resources are utilized with limited visibility of the machine health and asset availability. Thus, maintenance activities are organized even when they are not needed and machine parts are replaced before their useful life is over.

      • Not only does it make the total cost of assets and maintenance higher, but it also creates a system acceptance of inefficient asset management practices. Planned downtimes become the norm and plant teams become resistant to change. Without a decided shift in the approach for asset performance management, asset optimization can be very difficult to achieve.
Asset Optimization through Predictive Maintenance Predictive Maintenance solutions can help plant maintenance teams overcome the various challenges in asset performance management and ensure asset optimization across the plant. With a predictive approach, maintenance teams monitor asset conditions remotely with the help of cloud-enabled technologies. Vibration analysis, acoustics, thermography, oil analysis, and other remote condition monitoring techniques are deployed to track asset conditions while they operate as per schedule.

The machine health data is centrally collected and analyzed with the help of Industrial IoT (IIoT) technologies and accessible through responsive dashboards to concerned stakeholders. Since maintenance has to be strategized based on predictive insights, edge diagnostics, and advanced analytics are used to determine which asset is performing non-optimally and in need of attention. Such a focused approach to asset performance management has several benefits:
Conclusion In sum, asset optimization ensures that all available assets are utilized optimally in a manufacturing environment. By tracking asset conditions in real-time and performing predictive analytics, maintenance activities can be scheduled to optimize asset performance. Improved flow of information within the manufacturing organization and data-backed planning of asset maintenance can improve net return on assets (ROA) and overall plant productivity.

Infinite Uptime offers responsively designed predictive maintenance solutions in diverse industries such as Cement, Steel, Mining and Metals, Tire, Paper, Automotive, Chemicals, FMCG, Oil and Gas, and more. To understand how predictive maintenance applies to your process plant and can help in optimizing asset performance, explore the plant reliability solutions of Infinite Uptime.